Libraries required
library(plotly)
library(dplyr)
Data
load("input/limma_SC_Controls.RData")
lab <- dea.limma
load("input/limma_SC_Controls_lit.RData")
lit <- dea.limma
load("input/limma_SC_Controls_lab_newSeq.RData")
ns <- dea.limma
logFC comparison
PND7/8 vs PND14/15
plot_logFC <- function(lfc1, lfc2, n1, n2) {
library(plotly)
library(dplyr)
library(robcor)
rownames(lfc1) <- lfc1$Genes
rownames(lfc2) <- lfc2$Genes
l1 <- lfc1[abs(lfc1$logFC) >= 1 & lfc1$adj.P.Val <= 0.05, "Genes"]
l2 <- lfc2[abs(lfc2$logFC) >= 1 & lfc2$adj.P.Val <= 0.05, "Genes"]
genes <- union(l1, l2)
dat1 <- lfc1[genes, "logFC"]
names(dat1) <- rownames(lfc1[genes,])
dat2 <- lfc2[genes, "logFC"]
names(dat2) <- rownames(lfc2[genes,])
genes <- intersect(names(dat1)[!is.na(dat1)], names(dat2)[!is.na(dat2)])
fit <- lm(dat2[genes] ~ dat1[genes])
fitr <- MASS::rlm(dat2[genes] ~ dat1[genes])
plot_ly() %>%
add_trace(x = dat1[genes], y = dat2[genes], mode = "markers", name = "Genes") %>%
add_trace(x = dat1[genes], y = predict(fit), mode = "lines", name = "lm") %>%
add_trace(x = dat1[genes], y = predict(fitr), mode = "lines", name = "rlm") %>%
layout(
xaxis = list(title = n1),
yaxis = list(title = n2)
# title = paste("SC controls logFC:", n1, "and", n2)
)
}
lit: PND14 vs PND7 & old: PND15 vs PND8
plot_logFC(lfc1 = lab$`PND8 vs PND15`, lfc2 = lit$`PND7 vs PND14`, n1 = "old: PND15 vs PND8", n2 = "lit: PND14 vs PND7")
lit: PND14 vs PND7 & new: PND15 vs PND8
plot_logFC(lfc1 = ns$`pnd15 vs pnd8`, lfc2 = lit$`PND7 vs PND14`, n1 = "new: PND15 vs PND8", n2 = "lit: PND14 vs PND7")
old: PND15 vs PND8 and new: PND15 vs PND8
plot_logFC(lfc1 = ns$`pnd15 vs pnd8`, lfc2 = lab$`PND8 vs PND15`, n1 = "new: PND15 vs PND8", n2 = "old: PND15 vs PND8")
lit: PNW8 vs PND14 and new: PNW21 vs PND15
plot_logFC(lfc1 = ns$`pnw21 vs pnd15`, lfc2 = lit$`PND14 vs Adult`, n1 = "new: PNW21 vs PND15", n2 = "lit: PNW8 vs PND14")
References
report::cite_packages(session = sessionInfo())
## - C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
## - Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr
## - H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
## - Paul Smirnov (2014). robcor: Robust Correlations. R package version 0.1-6. https://CRAN.R-project.org/package=robcor
## - R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
SessionInfo
devtools::session_info() %>%
details::details()
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Ubuntu 16.04.7 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Zurich
date 2021-06-25
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.4)
bayestestR 0.9.0 2021-04-08 [1] CRAN (R 4.0.4)
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bslib 0.2.4 2021-01-25 [1] CRAN (R 4.0.4)
cachem 1.0.4 2021-02-13 [1] CRAN (R 4.0.4)
callr 3.7.0 2021-04-20 [1] CRAN (R 4.0.4)
cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.4)
clipr 0.7.1 2020-10-08 [1] CRAN (R 4.0.4)
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